An Anisotropic Velocity Model for Microseismic Events Localization in Tunnels
Author:
Shen Tong1, Wang Songren1, Jiang Xuan1, Peng Guili2, Tuo Xianguo3
Affiliation:
1. School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China 2. School of Control and Mechanical, Tianjin Chengjian University, Tianjin 300384, China 3. School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
Abstract
The velocity model is one of the main factors affecting the accuracy of microseismic event localization. This paper addresses the issue of the low accuracy of microseismic event localization in tunnels and, combined with active-source technology, proposes a “source–station” velocity model. The velocity model assumes that the velocity from the source to each station is different, and it can greatly improve the accuracy of the time-difference-of-arrival algorithm. At the same time, for the case of multiple active sources, the MLKNN algorithm was selected as the velocity model selection method through comparative testing. The results of numerical simulation and laboratory tests in the tunnel showed that the average location accuracy of the “source–station” velocity model was improved compared with that of the isotropic velocity and sectional velocity models, with numerical simulation experiments improving accuracy by 79.82% and 57.05% (from 13.28 m and 6.24 m to 2.68 m), and laboratory tests in the tunnel improving accuracy by 89.26% and 76.33% (from 6.61 m and 3.00 m to 0.71 m). The results of the experiments showed that the method proposed in this paper can effectively improve the location accuracy of microseismic events in tunnels.
Funder
Youth Science Foundation of the National Natural Science Foundation of China Youth Science Foundation of Sichuan Province Doctoral Fund of Southwest University of Science and Technology
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference30 articles.
1. Deformation monitoring and remote analysis of ultra-deep underground space excavation;Ren;Undergr. Space,2023 2. Challenges and Development Prospects of Ultra-Long and Ultra-Deep Mountain Tunnels;Zhu;Engineering,2019 3. Si, X., Peng, K., and Luo, S. (2022). Experimental Investigation on the Influence of Depth on Rockburst Characteristics in Circular Tunnels. Sensors, 22. 4. Zhang, H., Chen, L., Chen, S., Sun, J., and Yang, J. (2018). The Spatiotemporal Distribution Law of Microseismic Events and Rockburst Characteristics of the Deeply Buried Tunnel Group. Energies, 11. 5. Zhang, R., Gong, S., Dou, L., Cai, W., Li, X., Li, H., Tian, X., Ding, X., and Niu, J. (2023). Evaluation of Anti-Burst Performance in Mining Roadway Support System. Sensors, 23.
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